On Using Analogy to Reconcile Connections and Symbols

How do we gain both the standard advantages of connectionism and those of symbolic systems,
without adopting hybrid symbolic/connectionist systems? Fully connectionist systems that
support analogy-based reasoning are proposed as an answer, at least in the realm of high-level
cognitive processing. This domain includes commonsense reasoning and the semantic/pragmatic
aspects of natural language processing. The proposed type of system, purely by being
analogy-based, gains forms of graceful degradation, representation completion, similarity-based
generalization, learning, rule-emergence and exception-emergence. The system therefore gains
advantages commonly associated with connectionism, although the precise forms of the benefits
are different. At the same time, through being fully connectionist, the system also gains the
traditional connectionist variants of those advantages, as well as gaining further advantages not
provided by analogy-based reasoning per se. And, because the system is in part an implementation of a form of symbolic processing, it preserves the flexible handling of complex, temporary
structures that are well supported in traditional artificial intelligence and which are essential for
high-level cognitive processing. This chapter is in part a reaction against the excessive
polarization of the connectionism/symbolicism debate. This polarization is seen as resulting from
over-simplified, monolithic views both of what symbolic processing encompasses and of the
nature of the benefits that connectionism provides.